report · 2026 edition · australia & new zealand

We surveyed over 1,500 business leaders and technology professionals across Australia and New Zealand to understand how AI is actually showing up at work, from everyday use and productivity gains to ROI, policy, trust, manager readiness and the future workforce.

1,505
respondents
12+
industries
6
key themes
july '26
SURVEY PERIOD
01 — At a glance

↑53%

Of organisations have AI implemented in workflows or embedded in strategy, up from 13% a year ago

6×

Growth in businesses that have fully embedded AI into their strategy and operating model in 12 months, from 4% to 24%

8%

Just 8% of organisations are measuring AI's impact with clear metrics or KPIs

1 in 6

Only 16% are confident their staff know what data is safe to input into AI tools

02 — Who we heard from

Strong representation from government, technology, education, energy and resources, and financial services. The base includes C-suite leaders, directors, heads of function, managers and individual contributors, providing a broad view of how AI is being experienced across seniority and responsibility.

Location

Seniority

Industry

03 — Adoption & Maturity

Nearly half of those surveyed use AI daily for work, up from 34% last year. Weekly use has also increased with 71% using AI at least weekly compared to 60% in 2025.

However, while individual use has accelerated, organisational maturity is still uneven. Only a small proportion say AI is embedded in business strategy and operating model. Many have moved beyond early experimentation, but most are still applying AI in selected functions, workflows or business areas rather than embedding it into how the business operates.

↑49%

Use AI daily for work, up from 1 in 3 last year

↑53%

Have AI implemented in workflows or embedded in strategy, up from 13% in 2025

↓15%

Informal experimentation went down from 48% to 15% within a year

Organisation AI maturity

Year-on-year comparison suggests AI maturity is shifting from experimentation to implementation. However, full maturity remains limited and, while adoption has accelerated, AI transformation is still uneven.

Personal AI maturity

How respondents describe their personal AI use at work vs outside of work.

AI skills have shifted from a nice-to-have to a baseline expectation for the vast majority of roles we recruit. We’re actively advising candidates on how to keep building their AI capability and stay abreast of emerging trends.

Matthew Munson
Managing Director - Sydney
Talent

There's been a significant shift in AI maturity over the past year, with “not started” and informal experimentation dropping sharply while implementation in workflows and embedded strategy have grown. This suggests organisations are no longer just preparing for AI, but actively rolling it out and working through what it takes to make it part of how the business operates.

Jack Jorgensen
General Manager - Data, AI & Innovation
Avec
04 — Productivity & ROI

At an individual level, the productivity story is strong with respondents reporting greater efficiency, faster output, better quality and meaningful time savings. But for many organisations, those gains are not yet translating cleanly into measurable business impact. The challenge isn't just whether people are using AI, but whether it's improving the outcomes the business actually cares about.
85%

Say AI has made them more efficient at work

8%

Only 8% measure AI's impact with clear metrics or KPIs

26%

Use AI mainly to start work, but not finish it

Has AI made you more efficient at work?

Is your organisation formally measuring AI impact?

Metrics used to measure AI impact

The more precisely you measure AI usage, the more precisely you risk measuring the wrong thing. Stop asking how much AI people are using and start asking whether the outcomes the business already cared about — speed, quality, cost, risk and customer experience — are actually moving. If ROI becomes an adoption target, teams optimise for the metric, not the return.

Jack Jorgensen
General Manager - Data, AI & Innovation
Avec
05 — Policy & Risk

AI is being used for productivity faster than governance can keep up. While most respondents say their organisation's AI rules are clear, confidence drops when the question becomes more practical: do people actually understand what data can and cannot be entered into AI tools?

This is the productivity–policy gap. The risk isn't theoretical governance or deliberate misuse but everyday uncertainty: what information can be entered, confidential data exposure and decisions made without human oversight, pointing to a need for policies that are understood, reinforced and embedded into daily work.

57%

Name entering confidential or client data as a top risk behaviour

23%

Almost 1 in 4 say their organisation has never provided AI training or a mandatory policy refresh

16%

Just 16% are confident staff know what data is safe to enter into AI tools

Policy vs confidence

Respondents were asked how clear their organisation's rules were on AI use at work, and how confident they were that people in their organisation understood what data can and cannot be entered into AI tools.

Last AI training or policy refresh

Highest-risk AI behaviours

Policies create clarity, but they don't create consistent behaviour. Sustainable AI adoption comes from embedding good practice into everyday work through clear use cases, workflow guidance and regular reinforcement, so the responsible choice becomes the easiest choice. An AI framework should be the operating system for adoption, not a governance document that sits on the intranet.

Sarah Blanchard
Head of Talent Advisory
Solve

These figures are deeply concerning. If only 16% are very confident that people understand what data is safe to enter into AI tools, and 1 in 4 have never received AI training or a policy refresh, the risk isn't theoretical. Without stronger governance and practical training, organisations will likely see more incidents involving sensitive data, misuse of tools and avoidable exposure.

Jack Jorgensen
General Manager - Data, AI & Innovation
Avec
06 — Trust & Quality

In the age of ‘AI slop’, trust remains conditional. While 47% of respondents believe AI output quality is improving, their top concerns are loss of human judgement, data security, over-reliance and accuracy.

The conversation has moved beyond "Can AI do this?" to "When should we trust it, and where should humans stay in control?" AI is not being broadly rejected, but respondents are clear that its value depends on understanding its limits.

47%

Say AI output is getting better, and only 3% say it's getting worse

21%

Cite loss of human judgement as the biggest single concern

40%

Say AI is only useful when people know how to use it well

Is AI output improving or declining?

Biggest concerns about using AI at work

Are organisations becoming more realistic about AI's limits?

As models have improved over the past 12 months, so has the quality of their output, and the data reflects that. But models still hallucinate and have real limitations, so the 21% of respondents who identified loss of human judgement as their biggest concern are right to be cautious. It’s also concerning that 48% believe AI hype is still influencing business decisions, because the standards we apply to any major technology investment shouldn’t disappear just because AI is moving quickly.

Jack Jorgensen
General Manager - Data, AI & Innovation
Avec
07 — Managers

Managers sit at the intersection of AI adoption, quality control, productivity expectations, policy enforcement and team behaviour. While 76% feel at least somewhat prepared to manage AI use, the challenges they identify show how much complexity now sits with the management layer.

The issue is not just tool access or employee resistance. Managers are being asked to navigate data security, compliance, human judgement, overuse, poor use and productivity measurement, often while their own roles are changing too.

80%

Expect AI to reshape middle-management roles

1 in 4

Only 25% of managers feel very prepared to manage AI use in their team

14%

Say AI will increase pressure on managers without enough support

Manager preparedness

Hardest parts of managing AI use

Expected impact on middle management

It’s interesting that “training the team” ranks lowest, because stronger training would likely help ease many of the challenges above it, from data security and compliance to overuse, poor use and knowing when judgement is required. We’re seeing more AI enablement roles emerge but relying on one person or team to carry that responsibility is risky; managers are still the critical link between company objectives and day-to-day execution.

Cameron Robinson
Head of Enterprise
Solve
08 — Workforce Planning

AI is beginning to shape workforce planning, but most organisations are still focused on capability-building rather than full workforce redesign. Upskilling is the most common response, followed by reducing manual or repetitive work, while fewer organisations are redesigning roles, changing hiring priorities or using external partners to accelerate adoption.

This matters because AI transformation does not happen through tools alone. It requires new skills, clearer role design, revised workflows and a more deliberate view of where human capability and AI capability should sit.

44%

Are upskilling existing employees and is the top workforce response to AI

22%

Say their organisation is redesigning roles or workflows

26%

Haven't yet changed their workforce planning because of AI

How AI is changing workforce planning

From upskilling to restructuring

Broader workforce-planning maturity groups to demonstrate how organisations are responding to AI in 2026.

AI is changing what great looks like, and the real advantage sits with the organisations that back people who can combine AI with the technical expertise and commercial judgement to deliver better outcomes, not just faster outputs.

Thomas Mackenzie
Director of Client Services
Scale

Upskilling employees is essential, but training alone isn't enough. Organisations need to build people’s capability while also adapting the processes, workflows and governance around them. New technology only delivers meaningful value when people, process and governance evolve alongside it.

Jack Jorgensen
General Manager - Data, AI & Innovation
Avec
09 — key takeaways

For business leaders

The challenge now is proving whether AI is moving the outcomes that matter. Define success beyond usage and track the business measures you already care about: speed, quality, cost, risk, customer experience and workforce productivity. Otherwise, strong adoption can still become anecdotal impact.

For hiring managers

AI capability is becoming part of everyday performance. The value now is whether people can use AI critically, safely and effectively. Look for candidates who can show judgement, data awareness, workflow thinking and the ability to validate AI outputs.

For HR, P&C and TA leaders

The productivity-policy gap is now an enablement gap. Build practical guidance, manager capability and workflows that reinforce responsible AI use, because lasting behaviour change doesn't happen through policy alone.

For technology and transformation leaders

The gap between pilots and AI being fully embedded isn't just tool access but integration, governance and operating-model change. Prioritise use cases with measurable value, clear ownership, workflow integration and guardrails around data, quality and risk.

For managers and team leads

Expected to guide usage, manage quality, enforce policy and interpret productivity gains, managers are becoming the operational pressure point. Set clear expectations for when AI should be used, when outputs need checking, and where human judgement must remain in control.

For jobseekers and contractors

As a candidate, it isn't enough to list tools. Be prepared to demonstrate how you use AI to improve speed, quality and decision-making, while responsibly protecting data and checking outputs. Additional points if you can tie AI efficiency and impact with business goals.

what's next?

Whether you're hiring AI-capable talent, redesigning workflows or building a safer adoption strategy, our specialist teams can help.

Learn more about Avec →
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